Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Ying-Yi Hong & Jing-Han Chou, 2012. "Nonintrusive Energy Monitoring for Microgrids Using Hybrid Self-Organizing Feature-Mapping Networks," Energies, MDPI, vol. 5(7), pages 1-16, July.
- J. Stuart McMenamin & Frank A. Monforte, 1998. "Short Term Energy Forecasting with Neural Networks," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 43-61.
- Hong, Wei-Chiang, 2011. "Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm," Energy, Elsevier, vol. 36(9), pages 5568-5578.
- Hak-Man Kim & Tetsuo Kinoshita & Myong-Chul Shin, 2010. "A Multiagent System for Autonomous Operation of Islanded Microgrids Based on a Power Market Environment," Energies, MDPI, vol. 3(12), pages 1-19, December.
- Zhang, Wen Yu & Hong, Wei-Chiang & Dong, Yucheng & Tsai, Gary & Sung, Jing-Tian & Fan, Guo-feng, 2012. "Application of SVR with chaotic GASA algorithm in cyclic electric load forecasting," Energy, Elsevier, vol. 45(1), pages 850-858.
- Hak-Man Kim & Yujin Lim & Tetsuo Kinoshita, 2012. "An Intelligent Multiagent System for Autonomous Microgrid Operation," Energies, MDPI, vol. 5(9), pages 1-16, September.
- Zhe Xiao & Tinghua Li & Ming Huang & Jihong Shi & Jingjing Yang & Jiang Yu & Wei Wu, 2010. "Hierarchical MAS Based Control Strategy for Microgrid," Energies, MDPI, vol. 3(9), pages 1-17, September.
- J W Taylor & S Majithia, 2000. "Using combined forecasts with changing weights for electricity demand profiling," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(1), pages 72-82, January.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Wei Sun & Chongchong Zhang, 2018. "A Hybrid BA-ELM Model Based on Factor Analysis and Similar-Day Approach for Short-Term Load Forecasting," Energies, MDPI, vol. 11(5), pages 1-18, May.
- Hernández, Luis & Baladrón, Carlos & Aguiar, Javier M. & Carro, Belén & Sánchez-Esguevillas, Antonio & Lloret, Jaime, 2014. "Artificial neural networks for short-term load forecasting in microgrids environment," Energy, Elsevier, vol. 75(C), pages 252-264.
- Ganapathy Ramesh & Jaganathan Logeshwaran & Thangavel Kiruthiga & Jaime Lloret, 2023. "Prediction of Energy Production Level in Large PV Plants through AUTO-Encoder Based Neural-Network (AUTO-NN) with Restricted Boltzmann Feature Extraction," Future Internet, MDPI, vol. 15(2), pages 1-20, January.
- Yi Liang & Dongxiao Niu & Minquan Ye & Wei-Chiang Hong, 2016. "Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Improved Cuckoo Search," Energies, MDPI, vol. 9(10), pages 1-17, October.
- Feras Alasali & Khaled Nusair & Lina Alhmoud & Eyad Zarour, 2021. "Impact of the COVID-19 Pandemic on Electricity Demand and Load Forecasting," Sustainability, MDPI, vol. 13(3), pages 1-22, January.
- Jin-peng Liu & Chang-ling Li, 2017. "The Short-Term Power Load Forecasting Based on Sperm Whale Algorithm and Wavelet Least Square Support Vector Machine with DWT-IR for Feature Selection," Sustainability, MDPI, vol. 9(7), pages 1-20, July.
- Seung Wan Kim & Jip Kim & Young Gyu Jin & Yong Tae Yoon, 2016. "Optimal Bidding Strategy for Renewable Microgrid with Active Network Management," Energies, MDPI, vol. 9(1), pages 1-15, January.
- Li, Wei-Qin & Chang, Li, 2018. "A combination model with variable weight optimization for short-term electrical load forecasting," Energy, Elsevier, vol. 164(C), pages 575-593.
- Chan-Uk Yeom & Keun-Chang Kwak, 2017. "Short-Term Electricity-Load Forecasting Using a TSK-Based Extreme Learning Machine with Knowledge Representation," Energies, MDPI, vol. 10(10), pages 1-18, October.
- Lianhui Li & Chunyang Mu & Shaohu Ding & Zheng Wang & Runyang Mo & Yongfeng Song, 2015. "A Robust Weighted Combination Forecasting Method Based on Forecast Model Filtering and Adaptive Variable Weight Determination," Energies, MDPI, vol. 9(1), pages 1-22, December.
- Peplinski, McKenna & Dilkina, Bistra & Chen, Mo & Silva, Sam J. & Ban-Weiss, George A. & Sanders, Kelly T., 2024. "A machine learning framework to estimate residential electricity demand based on smart meter electricity, climate, building characteristics, and socioeconomic datasets," Applied Energy, Elsevier, vol. 357(C).
- Seunghyoung Ryu & Jaekoo Noh & Hongseok Kim, 2016. "Deep Neural Network Based Demand Side Short Term Load Forecasting," Energies, MDPI, vol. 10(1), pages 1-20, December.
- Amrutha Raju Battula & Sandeep Vuddanti & Surender Reddy Salkuti, 2021. "Review of Energy Management System Approaches in Microgrids," Energies, MDPI, vol. 14(17), pages 1-32, September.
- Akash Kumar & Bing Yan & Ace Bilton, 2022. "Machine Learning-Based Load Forecasting for Nanogrid Peak Load Cost Reduction," Energies, MDPI, vol. 15(18), pages 1-23, September.
- Luis Hernández & Carlos Baladrón & Javier M. Aguiar & Lorena Calavia & Belén Carro & Antonio Sánchez-Esguevillas & Pablo García & Jaime Lloret, 2013. "Experimental Analysis of the Input Variables’ Relevance to Forecast Next Day’s Aggregated Electric Demand Using Neural Networks," Energies, MDPI, vol. 6(6), pages 1-22, June.
- Jaime Buitrago & Shihab Asfour, 2017. "Short-Term Forecasting of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks with Exogenous Vector Inputs," Energies, MDPI, vol. 10(1), pages 1-24, January.
- Svetlana Solodusha & Mikhail Bulatov, 2021. "Integral Equations Related to Volterra Series and Inverse Problems: Elements of Theory and Applications in Heat Power Engineering," Mathematics, MDPI, vol. 9(16), pages 1-18, August.
- Andrea Bonfiglio & Massimo Brignone & Marco Invernizzi & Alessandro Labella & Daniele Mestriner & Renato Procopio, 2017. "A Simplified Microgrid Model for the Validation of Islanded Control Logics," Energies, MDPI, vol. 10(8), pages 1-28, August.
- Cheng-Ming Lee & Chia-Nan Ko, 2016. "Short-Term Load Forecasting Using Adaptive Annealing Learning Algorithm Based Reinforcement Neural Network," Energies, MDPI, vol. 9(12), pages 1-15, November.
- Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2018. "Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †," Energies, MDPI, vol. 11(7), pages 1-20, June.
- Thomas Price & Gordon Parker & Gail Vaucher & Robert Jane & Morris Berman, 2022. "Microgrid Energy Management during High-Stress Operation," Energies, MDPI, vol. 15(18), pages 1-11, September.
- Yuqi Dong & Xuejiao Ma & Chenchen Ma & Jianzhou Wang, 2016. "Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting," Energies, MDPI, vol. 9(12), pages 1-30, December.
- Powell, Kody M. & Sriprasad, Akshay & Cole, Wesley J. & Edgar, Thomas F., 2014. "Heating, cooling, and electrical load forecasting for a large-scale district energy system," Energy, Elsevier, vol. 74(C), pages 877-885.
- Feras Alasali & Husam Foudeh & Esraa Mousa Ali & Khaled Nusair & William Holderbaum, 2021. "Forecasting and Modelling the Uncertainty of Low Voltage Network Demand and the Effect of Renewable Energy Sources," Energies, MDPI, vol. 14(8), pages 1-31, April.
- Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2019. "Single and Multi-Sequence Deep Learning Models for Short and Medium Term Electric Load Forecasting," Energies, MDPI, vol. 12(1), pages 1-21, January.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Xi Wu & Ping Jiang & Jing Lu, 2014. "Multiagent-Based Distributed Load Shedding for Islanded Microgrids," Energies, MDPI, vol. 7(9), pages 1-13, September.
- Ming-Tse Kuo & Shiue-Der Lu, 2013. "Design and Implementation of Real-Time Intelligent Control and Structure Based on Multi-Agent Systems in Microgrids," Energies, MDPI, vol. 6(11), pages 1-15, November.
- Zhu, L. & Li, M.S. & Wu, Q.H. & Jiang, L., 2015. "Short-term natural gas demand prediction based on support vector regression with false neighbours filtered," Energy, Elsevier, vol. 80(C), pages 428-436.
- Min-Liang Huang, 2016. "Hybridization of Chaotic Quantum Particle Swarm Optimization with SVR in Electric Demand Forecasting," Energies, MDPI, vol. 9(6), pages 1-16, May.
- Deihimi, Ali & Orang, Omid & Showkati, Hemen, 2013. "Short-term electric load and temperature forecasting using wavelet echo state networks with neural reconstruction," Energy, Elsevier, vol. 57(C), pages 382-401.
- Zhirong Xu & Ping Yang & Zhiji Zeng & Jiajun Peng & Zhuoli Zhao, 2016. "Black Start Strategy for PV-ESS Multi-Microgrids with Three-Phase/Single-Phase Architecture," Energies, MDPI, vol. 9(5), pages 1-14, May.
- Wei-Tzer Huang & Tsai-Hsiang Chen & Hong-Ting Chen & Jhih-Siang Yang & Kuo-Lung Lian & Yung-Ruei Chang & Yih-Der Lee & Yuan-Hsiang Ho, 2015. "A Two-stage Optimal Network Reconfiguration Approach for Minimizing Energy Loss of Distribution Networks Using Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 8(12), pages 1-17, December.
- Thai-Thanh Nguyen & Hyeong-Jun Yoo & Hak-Man Kim, 2015. "A Flywheel Energy Storage System Based on a Doubly Fed Induction Machine and Battery for Microgrid Control," Energies, MDPI, vol. 8(6), pages 1-16, June.
- Cheng-Wen Lee & Bing-Yi Lin, 2016. "Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting," Energies, MDPI, vol. 9(11), pages 1-16, October.
- Yujin Lim & Hak-Man Kim & Tetsuo Kinoshita, 2014. "Distributed Load-Shedding System for Agent-Based Autonomous Microgrid Operations," Energies, MDPI, vol. 7(1), pages 1-17, January.
- Van-Hai Bui & Akhtar Hussain & Hak-Man Kim, 2017. "Optimal Operation of Microgrids Considering Auto-Configuration Function Using Multiagent System," Energies, MDPI, vol. 10(10), pages 1-16, September.
- Yan Hong Chen & Wei-Chiang Hong & Wen Shen & Ning Ning Huang, 2016. "Electric Load Forecasting Based on a Least Squares Support Vector Machine with Fuzzy Time Series and Global Harmony Search Algorithm," Energies, MDPI, vol. 9(2), pages 1-13, January.
- Moting Su & Zongyi Zhang & Ye Zhu & Donglan Zha & Wenying Wen, 2019. "Data Driven Natural Gas Spot Price Prediction Models Using Machine Learning Methods," Energies, MDPI, vol. 12(9), pages 1-17, May.
- Hak-Man Kim & Yujin Lim & Tetsuo Kinoshita, 2012. "An Intelligent Multiagent System for Autonomous Microgrid Operation," Energies, MDPI, vol. 5(9), pages 1-16, September.
- Ning Zhang & Wei Gu & Haojun Yu & Wei Liu, 2013. "Application of Coordinated SOFC and SMES Robust Control for Stabilizing Tie-Line Power," Energies, MDPI, vol. 6(4), pages 1-16, April.
- Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
- Nah-Oak Song & Ji-Hye Lee & Hak-Man Kim & Yong Hoon Im & Jae Yong Lee, 2015. "Optimal Energy Management of Multi-Microgrids with Sequentially Coordinated Operations," Energies, MDPI, vol. 8(8), pages 1-20, August.
- Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
- Cao, Guohua & Wu, Lijuan, 2016. "Support vector regression with fruit fly optimization algorithm for seasonal electricity consumption forecasting," Energy, Elsevier, vol. 115(P1), pages 734-745.
- Nah-Oak Song & Ji-Hye Lee & Hak-Man Kim, 2016. "Optimal Electric and Heat Energy Management of Multi-Microgrids with Sequentially-Coordinated Operations," Energies, MDPI, vol. 9(6), pages 1-18, June.
More about this item
Keywords
artificial neural network; distributed intelligence; short-term load forecasting; smart grid; microgrid; multilayer perceptron;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:6:y:2013:i:3:p:1385-1408:d:24008. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.